






'''simple docstring'''







import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


__lowerCAmelCase        =							["small", "medium", "large"]

__lowerCAmelCase        =							"lm_head.decoder.weight"
__lowerCAmelCase        =							"lm_head.weight"


def       __UpperCamelCase					(      lowercase_       :  str   ,       lowercase_       :  str   ):
       """simple docstring"""




       a_						     =			torch.load(lowercase_   )
       a_						     =			d.pop(lowercase_   )
       os.makedirs(lowercase_   ,       exist_ok=lowercase_   )
       torch.save(lowercase_   ,       os.path.join(lowercase_   ,       lowercase_   )   )


if __name__ == "__main__":
  __lowerCAmelCase        =							argparse.ArgumentParser()
  parser.add_argument("--dialogpt_path", default=".", type=str)
  __lowerCAmelCase        =							parser.parse_args()
  for MODEL in DIALOGPT_MODELS:
    __lowerCAmelCase        =							os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
    __lowerCAmelCase        =							f"""./DialoGPT-{MODEL}"""
    convert_dialogpt_checkpoint(
        checkpoint_path,
        pytorch_dump_folder_path,
    )












'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCAmelCase        =							{"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase        =							["GLPNFeatureExtractor"]
  __lowerCAmelCase        =							["GLPNImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase        =							[
      "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
      "GLPNForDepthEstimation",
      "GLPNLayer",
      "GLPNModel",
      "GLPNPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_glpn import GLPNFeatureExtractor
    from .image_processing_glpn import GLPNImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_glpn import (
        GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
        GLPNForDepthEstimation,
        GLPNLayer,
        GLPNModel,
        GLPNPreTrainedModel,
    )


else:
  import sys

  __lowerCAmelCase        =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




